31 Aug, 2018 at 10:15 | Posted in Economics | Comments Off on After the crisis — business as usual

In contrast to the experience of the Great Depression, which led to the emergence and acceptance of novel theoretical concepts on a large scale, the financial crisis and its consequences have, by and large, been rationalized with reference to existing theoretical concepts. Although we do observe a slight shift away from the idea that financial markets are efficient by default and prices only follow random walks, the basic conceptualization of (financial) markets as being efficient and equilibrating in principle seems unquestioned. On the contrary, the rising prominence of the concept of “liquidity” – understood as the availability of funds to absorb financial assets to be sold – in the aftermath of the crisis indicates that the financial crisis is seen by economists as a major external shock, unforeseen because of the limits imposed on rational behavior by asymmetric information, and not as something intrinsic to the economic process. Similarly, our analysis of the reception of major crisis-related books shows an only temporary increase of interest in classic contributions dealing with financial and economic instability, which was even weaker for more distinguished journals. These observations signify a key difference in terms of the ‘lessons learned’ from past crises when compared to the Great Depression, which gave rise to a broad consensus that capitalist economies are not self-sustaining, a consensus that eventually helped to forge the mixed economies dominating the richer parts of the planet.

There are many kinds of useless economics held in high regard within mainstream economics establishment today. Few — if any — are less deserved than the macroeconomic theory/method — mostly connected with Nobel laureates Finn Kydland, Robert Lucas, Edward Prescott and Thomas Sargent — called calibration.

In physics, it may possibly not be straining credulity too much to model processes as ergodic – where time and history do not really matter – but in social and historical sciences it is obviously ridiculous. If societies and economies were ergodic worlds, why do econometricians fervently discuss things such as structural breaks and regime shifts? That they do is an indication of the unrealisticness of treating open systems as analyzable with ergodic concepts.

The future is not reducible to a known set of prospects. It is not like sitting at the roulette table and calculating what the future outcomes of spinning the wheel will be. Reading Sargent and other calibrationists one comes to think of Robert Clower’s apt remark that

much economics is so far removed from anything that remotely resembles the real world that it’s often difficult for economists to take their own subject seriously.

Instead of just assuming calibration and rational expectations to be right, one ought to confront the hypothesis with the available evidence. It is not enough to construct models. Anyone can construct models. To be seriously interesting, models have to come with an aim. They have to have an intended use. If the intention of calibration and rational expectations is to help us explain real economies, it has to be evaluated from that perspective. A model or hypothesis without a specific applicability is not really deserving of our interest.

To say, as Edward Prescott,that

one can only test if some theory, whether it incorporates rational expectations or, for that matter, irrational expectations, is or is not consistent with observations

is not enough. Without strong evidence, all kinds of absurd claims and nonsense may pretend to be science. We have to demand more of a justification than this rather watered-down version of ‘anything goes’ when it comes to rationality postulates. If one proposes rational expectations one also has to support its underlying assumptions. None is given, which makes it rather puzzling how rational expectations has become the standard modelling assumption made in much of modern macroeconomics. Perhaps the reason is, as Paul Krugman has it, that economists often mistake

beauty, clad in impressive-looking​ mathematics, for truth.

But I think Prescott’s view is also the reason why calibration economists are not particularly interested in empirical examinations of how real choices and decisions are made in real economies. In the hands of Lucas, Prescott and Sargent, rational expectations has been transformed from an – in principle – testable hypothesis to an irrefutable proposition. Irrefutable propositions may be comfortable — like religious convictions or ideological dogmas — but it is not science.

28 Aug, 2018 at 14:22 | Posted in Statistics & Econometrics | Comments Off on Some common misunderstandings about randomization

Randomization is an alternative when we do not know enough to control, but is generally inferior to good control when we do. We suspect that at least some of the popular and professional enthusiasm for RCTs, as well as the belief that they are precise by construction, comes from misunderstandings about … random or realized confounding on the one hand and confounding in expectation on the other …

The RCT strategy is only successful if we are happy with estimates that are arbitrarily far from the truth, just so long as the errors cancel out over a series of imaginary experiments. In reality, ​the causality that is being attributed to the treatment might, in fact, be coming from an imbalance in some other cause in our particular trial; limiting this requires serious thought about possible covariates.

The point of making a randomized experiment is often said to be that it ‘ensures’ that any correlation between a supposed cause and effect indicates a causal relation. This is believed to hold since randomization (allegedly) ensures that a supposed causal variable does not correlate with other variables that may influence the effect.

The problem with that simplistic view on randomization is that the claims made are both exaggerated and false:

• Even if you manage to do the assignment to treatment and control groups ideally random, the sample selection certainly is — except in extremely rare cases — not random. Even if we make a proper randomized assignment, if we apply the results to a biased sample, there is always the risk that the experimental findings will not apply. What works ‘there,’ does not work ‘here.’ Randomization hence does not ‘guarantee ‘ or ‘ensure’ making the right causal claim. Although randomization may help us rule out certain possible causal claims, randomization per se does not guarantee anything!

• Even if both sampling and assignment are made in an ideal random way, performing standard randomized experiments only give you averages. The problem here is that although we may get an estimate of the ‘true’ average causal effect, this may ‘mask’ important heterogeneous effects of a causal nature. Although we get the right answer of the average causal effect being 0, those who are ‘treated’ may have causal effects equal to -100 and those ‘not treated’ may have causal effects equal to 100. Contemplating being treated or not, most people would probably be interested in knowing about this underlying heterogeneity and would not consider the average effect particularly enlightening.

• There is almost always a trade-off between bias and precision. In real-world settings, a little bias often does not overtrump greater precision. And — most importantly — in case we have a population with sizeable heterogeneity, the average treatment effect of the sample may differ substantially from the average treatment effect in the population. If so, the value of any extrapolating inferences made from trial samples to other populations is highly questionable.

• Since most real-world experiments and trials build on performing a single randomization, what would happen if you kept on randomizing forever, does not help you to ‘ensure’ or ‘guarantee’ that you do not make false causal conclusions in the one particular randomized experiment you actually do perform. It is indeed difficult to see why thinking about what you know you will never do, would make you happy about what you actually do.

Randomization is not a panacea. It is not the best method for all questions and circumstances. Proponents of randomization make claims about its ability to deliver causal knowledge that are simply wrong. There are good reasons to be sceptical of the now popular — and ill-informed — view that randomization is the only valid and best method on the market. It is not.

That is the great thing about abstraction. Working with what can be called ‘flex price’ models does not imply that you think price rigidity is unimportant, but instead that it can often be ignored if you want to focus on other processes.

When applying deductivist thinking to economics, mainstream economists like Wren-Lewis usually set up ‘as if’ models based on a set of tight axiomatic assumptions from which consistent and precise inferences are made. The beauty of this procedure is, of course,​ that if the axiomatic premises are true, the conclusions necessarily follow. The snag is that if the models are to be relevant, we also have to argue that their precision and rigour still holds when they are applied to real-world situations. They often do not. When addressing real economies, the idealizations necessary for the deductivist machinery to work — as e. g. ‘flex price’ models — simply do not hold.

If the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? The logic of idealization is a marvellous tool in mathematics and axiomatic-deductivist systems, but a poor guide for action in real-world systems, in which concepts and entities are without clear boundaries and continually interact and overlap.

The neoclassical style of thought – with its emphasis on thought experiments, reflection on the basis of illustrative examples and logically possible extreme cases, its use of model construction as the basis of plausible assumptions, as well as its tendency to decrease the level of abstraction, and similar procedures — appears to have had such a strong influence on economic methodology that even theoreticians who strongly value experience can only free themselves from this methodology with difficulty …

Clearly, it is possible to interpret the ‘presuppositions’ of a theoretical system not as hypotheses, but simply as limitations to the area of application of the system in question. Since a relationship to reality is usually ensured by the language used in economic statements, in this case the impression is generated that a content-laden statement about reality is being made, although the system is fully immunized and thus without content. In my view that is often a source of self-deception in pure economic thought …

A further possibility for immunizing theories consists in simply leaving open the area of application of the constructed model so that it is impossible to refute it with counter examples. This of course is usually done without a complete knowledge of the fatal consequences of such methodological strategies for the usefulness of the theoretical conception in question, but with the view that this is a characteristic of especially highly developed economic procedures: the thinking in models, which, however, among those theoreticians who cultivate neoclassical thought, in essence amounts to a new form of Platonism.

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